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In the area of fewshot anomaly detection (FSAD), efficient visual feature plays an essential role in memory bank M-based methods.
Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun · 2013
Earlier work this paper cites.
Exploiting cyclic symmetry in convolutional neural networks
Sander Dieleman, Jeffrey De Fauw, and Koray Kavukcuoglu · 2016
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Identity mappings in deep residual networks
Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas Kipf and Max Welling · 2017
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2018
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Mvtec ad — a comprehensive real-world dataset for unsupervised anomaly detection
Paul Bergmann, Michael Fauser, David Sattlegger, and Carsten Steger · 2019
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Guohao Li, Matthias Müller, Ali K. Thabet, and Bernard Ghanem · 2019
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Sub-image anomaly detection with deep pyramid correspondences
Niv Cohen and Yedid Hoshen · 2020
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Fast haar transforms for graph neural networks
Ming Li, Zheng Ma, Yu Guang Wang, and Xiaosheng Zhuang · 2020
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Explainable deep one-class classification
Philipp Liznerski, Lukas Ruff, Robert A Vandermeulen, Billy Joe Franks, Marius Kloft, and Klaus Robert Muller · 2020
Cited alongside, same era.
Haar graph pooling
Yu Guang Wang, Ming Li, Zheng Ma, Guido Montufar, Xiaosheng Zhuang, and Yanan Fan · 2020
Cited alongside, same era.
Padim: a patch distribution modeling framework for anomaly detection and localization
Learning unsupervised metaformer for anomaly detection
Jhih-Ciang Wu, Ding-Jie Chen, Chiou-Shann Fuh, and Tyng-Luh Liu · 2021
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Beyond dents and scratches: Logical constraints in unsupervised anomaly detection and localization
Paul Bergmann, Kilian Batzner, Michael Fauser, David Sattlegger, and Carsten Steger · 2022
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Anomaly detection via reverse distillation from one-class embedding
Hanqiu Deng and Xingyu Li · 2022
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Catching both gray and black swans: Open-set supervised anomaly detection
Choubo Ding, Guansong Pang, and Chunhua Shen · 2022
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Vision gnn: An image is worth graph of nodes
Kai Han, Yunhe Wang, Jianyuan Guo, Yehui Tang, and Enhua Wu · 2022
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Thomas Defard, Aleksandr Setkov, Angelique Loesch, and Romaric Audigier · 2021
Cited alongside, same era.
Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions
Stepan Jezek, Martin Jonak, Radim Burget, Pavel Dvorak, and Milos Skotak · 2021
Cited alongside, same era.
Explainable deep few-shot anomaly detection with deviation networks
Guansong Pang, Choubo Ding, Chunhua Shen, and Anton van den Hengel · 2021
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter
Cited in the paper.
Student-teacher feature pyramid matching for anomaly detection
Guodong Wang, Shumin Han, Errui Ding, and Di Huang
Cited in the paper.
Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao
Cited in the paper.
Registration based few-shot anomaly detection
Chaoqin Huang, Haoyan Guan, Aofan Jiang, Ya Zhang, Michael Spratling, and Yan-Feng Wang · 2022
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Cfa: Coupled-hypersphere-based feature adaptation for target-oriented anomaly localization
Sungwook Lee, Seunghyun Lee, and Byung Cheol Song · 2022
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Towards total recall in industrial anomaly detection
Karsten Roth, Latha Pemula, Joaquin Zepeda, Bernhard Schölkopf, Thomas Brox, and Peter Gehler · 2022
Later among the works it cites.